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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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230 records · Page 13

Tritium diffusion and formation in the bulk and defective surface of γ-LiAlO2 pellets: First-principles investigation

In tritium-producing burnable absorber rods (TPBAR), γ-LiAlO2 is used in the form of an annular ceramic pellet enriched with the 6Li isotope. When irradiated in a pressurized water reactor (PWR), the <sup>6</sup>Li pellets absorb neutrons and produce tritium (<sup>3</sup>H) through <sup>6</sup>Li + n <sup>3</sup>H + α. The 3H chemically reacts with the metal getter where it is captured and leads to formation of a metal hydride. For TPBARs to enable effective tritium production in PWRs, we investigated the <sup>3</sup>H diffusion pathways in the bulk and surface of γ-LiAlO<sub>2</sub> with different concentrations of lithium defects. The calculated results for bulk and low-index surfaces, thermal conductivity, 3H activation energy barriers, and the 3H diffusion coefficients in γ-LiAlO<sub>2</sub> are in good agreement with the available experimental values. In the bulk, our results show that the smallest activation energy barrier is 0.63 eV for substitutional <sup>3</sup>H diffusion with a diffusion coefficient of 3.25x10<sup>-12</sup> m<sup>2</sup>/s. After <sup>3</sup>H diffused from bulk to the surface, it could form different species (such as <sup>3</sup>H<sub>2</sub>, <sup>3</sup>H<sub>2</sub>O, C<sup>3</sup>H<sub>4</sub>) depending on the surface structure, vacancy types and the impurity carbon. Our results indicate that the <sup>3</sup>H<sub>2</sub> is the main product from γ-LiAlO<sub>2</sub> pellets. As the number of V<sub>Li</sub> vacancies and <sup>3</sup>H atoms increases under irradiation (<sup>3</sup>H-rich condition), <sup>3</sup>H<sub>2</sub>O release could increase from the surface of γ-LiAlO<sub>2</sub> pellets.

Jia, Ting↗

Passively pumped, polycrystalline ceramic high and ultra-high vacuum chambers

A sealed, passively pumped, polycrystalline ceramic vacuum chamber and method for fabricating the chamber are disclosed. The body of the vacuum chamber is made from a polycrystalline ceramic, for example, alumina. The vacuum chamber includes one or more windows made from a transparent ceramic, for example, sapphire, to accommodate optical access, while remaining amorphous-glass free to minimize or eliminate helium permeation. The vacuum chamber components are joined via laser welding or furnace brazing and the completed chamber is bakeable at temperatures up to 400° C. The vacuum chamber can operate at high or ultra-high vacuum pressures for an extended period through the use of one or more getter-based pumps. The vacuum chamber may include one or more atomic sources depending upon the application.

Walker, Charles A.↗

Analysis of outgassing from natural fibers VIP core to improve their long-term performance

Buildings are the largest consumers of energy accounting for 40% of the total energy use in the US. The use of vacuum insulation panels (VIPs) with large R/in. values offers unique benefits in reducing building energy consumption such as providing excellent thermal insulation with significantly less thickness compared to convention insulation materials. Natural fibers are promising VIP core material because of their low cost and offer good thermal performance. However, the long-term thermal performance of the VIPs with natural fiber core can be compromised because of off-gassing from natural fibers. In this study, we utilize a mass spectrometer based analytical technique to investigate the composition of off-gassing from natural fibers. The result can be used to determine the optimal getters to help maintain the vacuum and improve the long-term thermal performance of the VIP for building insulation.

Tamraparni, Achutha [ORNL] (ORCID:0000000299658544↗

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating a closed-form predictive distribution over outputs informed by non-identically distributed training data. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example out-of-training-sample. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy—referred to as information sharing—that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

17 WIND ENERGY↗

Statistical Treatment of Convolutional Neural Network Superresolution of Inland Surface Wind for Subgrid-Scale Variability Quantification

Abstract Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge of recovering fine-scale information from coarse data. This limits their compatibility with many global climate model outputs, often available between ∼50- and 100-km resolution, to scales of interest such as cloud resolving or urban scales. This study systematically examines the capability of a type of superresolving convolutional neural network (SR-CNNs) to downscale surface wind speed data over land from different coarse resolutions (25-, 48-, and 100-km resolution) to 3 km. For each downscaling factor, we consider three convolutional neural network (CNN) configurations that generate superresolved predictions of fine-scale wind speed, which take between one and three input fields: coarse wind speed, fine-scale topography, and diurnal cycle. In addition to fine-scale wind speeds, probability density function parameters are generated through which sample wind speeds can be generated, accounting for the intrinsic stochasticity of wind speed. For assessing generalization to new data, CNN models are tested on regions with different topography and climate that are unseen during training. The evaluation of superresolved predictions focuses on subgrid-scale variability and the recovery of extremes. Models with coarse wind and fine topography as inputs exhibit the best performance when compared with other model configurations, operating across the same downscaling factor. Our diurnal cycle encoding results in lower out-of-sample generalizability when compared with other input configurations.

17 WIND ENERGY↗

Transfer of EP and Doping Technology for PIP-II HB650 Cavities from Fermilab to Industry

Fermilab has optimized the surface processing conditions for PIP-II high beta 650 MHz cavities. This encompasses conditions for bulk electropolishing, heat treatment, nitrogen doping, post-doping final electropolishing, and post-processing surface rinsing. The technology has been effectively transitioned to industry. This paper highlights the efforts made to fine-tune the process and to smoothly share them with the partner labs and an associated vendor.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOI Use of AVIRIS Data in Natural Resources Management: A Technology Transfer Project Status Report

A meeting was held in December 1996, attended by representatives from Department of the Interior (DOI) and National Aeronautical and Space Administration (NASA) at the request of Department of the Interior Secretary, Bruce Babbitt and NASA, Administrator Director Dan Goldin, to discuss the use of hyperspectral systems and related remote sensing technologies in addressing environmental issues of environmental importance to the (DOI). It was determined that NASA/DOI Coordination be established, comprised of representatives from each agency, designated to address the environmental issues and resulting technologies. A steering committee was formed consisting of representatives from NASA Headquarters, NASA PI's from University California-Davis, University of Colorado-Boulder, University of New Hampshire and DOI Bureaus (Indian Affairs, Land Management, Reclamation, National Park Service, and US Geological Survey).

Getter, James R.↗

Preliminary Testing of NASA's Molecular Adsorber Coating Technology for Future Missions to Mars

The Molecular Adsorber Coating (MAC) is a sprayable coatings technology that was developed at NASA Goddard Space Flight Center (GSFC). The coating is comprised of highly porous, zeolite materials that help capture outgassed molecular contaminants on spaceflight applications. The adsorptive capabilities of the coating can alleviate molecular contamination concerns on or near sensitive surfaces and instruments within a spacecraft. This paper will discuss the preliminary testing of NASA’s MAC technology for use on future missions to Mars. The study involves evaluating the coating’s molecular adsorption properties in simulated test conditions, which include the vacuum environment of space and the Martian atmosphere. MAC adsorption testing was performed using a commonly used plasticizer called dioctyl phthalate (DOP) as the test contaminant.

CO2↗

Preliminary Testing of NASA's Molecular Adsorber Coating Technology for Future Missions to Mars

The Molecular Adsorber Coating (MAC) is a sprayable coatings technology that was developed at NASA Goddard Space Flight Center (GSFC). The coating is comprised of highly porous, zeolite materials that help capture outgassed molecular contaminants on spaceflight applications. The adsorptive capabilities of the coating can alleviate molecular contamination concerns on or near sensitive surfaces and instruments within a spacecraft. This paper will discuss the preliminary testing of NASA's MAC technology for use on future missions to Mars. The study involves evaluating the coating's molecular adsorption properties in simulated test conditions, which include the vacuum environment of space and the Martian atmosphere. MAC adsorption testing was performed using a commonly used plasticizer called dioctyl phthalate (DOP) as the test contaminant.

outgassing↗

Using NASA's Molecular Adsorber Coating Technology During Thermal Vacuum Testing to Protect Critical Laser Flight Optics on the ATLAS Instrument

The Molecular Adsorber Coating (MAC) is a sprayable coatings technology that was developed at NASA Goddard Space Flight Center (GSFC). The coating was designed to address molecular contamination concerns on or near sensitive surfaces and instruments within the spacecraft for flight or ground-based applications in vacuum conditions. This paper will discuss the use of NASA's MAC technology to isolate and protect the critical laser flight optics of the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). MAC was strategically used during thermal vacuum (TVAC) testing efforts to reduce the risk of contaminating the laser optical components from non-baked items and other unknown outgassing sources from the chamber environment. This paper summarizes the design and implementation efforts, and the chemical analysis of the MAC samples that were used during two recent TVAC tests for the ICESat-2/ATLAS mission.

molecular adsorber coating↗